Method and system for integrated well construction

ABSTRACT

A method may include obtaining, by the computer processor, rig operation data regarding various drilling rigs at different geographic locations. The method may further include generating, using the rig operation data, a model that identifies a level of risk associated with various rig operations. The method may further include simulating, by the computer processor and using the geographic location data and the model, a sequence of rig operations for constructing a portion of a wellbore drilled by a drilling rig at the desired geographic location.

BACKGROUND

Prior to submitting a bid for a drilling project, a contractor mayitemize the individual costs of the drilling project in preparation forsubmitting a formal bid. However, during actual construction of thedrilling project, cost overruns may occur due to unforeseen issues notincluded in the formal bid. For example, if certain control systemsmalfunction, nonproductive time may occur during the construction on thedrilling project. Thus, with a particular bid, there exists variousrisks for the contractor inherent to winning the bid for the drillingproject. Accordingly, technologies are desired that can quantify theserisks with precision.

SUMMARY

In general, in one aspect, embodiments relate to a method that includesobtaining, by a computer processor, geographic location data regarding adesired geographic location for a drilling rig. The method includesobtaining, by the computer processor, rig operation data regardingvarious drilling rigs at different geographic locations. The methodincludes generating, using the rig operation data, a model thatidentifies a level of risk associated with various rig operations. Themethod includes simulating, by the computer processor and using thegeographic location data and the model, a sequence of rig operations forconstructing a portion of a wellbore drilled by a drilling rig at thedesired geographic location.

In general, in one aspect, embodiments relate to a system that includesa computer processor. The system includes a memory coupled to thecomputer processor and executable by the computer processor. The memoryincludes functionality for obtaining, by the computer processor,geographic location data regarding of a desired geographic location fora drilling rig. The memory includes functionality for obtaining, by thecomputer processor, rig operation data regarding various drilling rigsat different geographic locations. The memory includes functionality forgenerating, using the rig operation data, a model that identifies alevel of risk associated with various rig operations. The memoryincludes functionality for simulating, using the geographic locationdata and the model, a sequence of rig operations for constructing aportion of a wellbore drilled by the drilling rig at the desiredgeographic location.

In general, in one aspect, embodiments relate to a non-transitorycomputer readable medium storing instructions executable by a computerprocessor. The instructions include functionality for obtaining, by acomputer processor, geographic location data regarding of a desiredgeographic location for a drilling rig. The instructions includefunctionality for obtaining, by the computer processor, rig operationdata regarding various drilling rigs at different geographic locations.The instructions include functionality for generating, using the rigoperation data, a model that identifies a level of risk associated withvarious rig operations. The instructions include functionality forsimulating, using the geographic location data and the model, a sequenceof rig operations for constructing a portion of a wellbore drilling bythe drilling rig at the desired geographic location.

Other aspects of the disclosure will be apparent from the followingdescription and the appended claims.

BRIEF DESCRIPTION OF DRAWINGS

Specific embodiments of the disclosed technology will now be describedin detail with reference to the accompanying figures. Like elements inthe various figures are denoted by like reference numerals forconsistency.

FIG. 1 shows a block diagram of a system in accordance with one or moreembodiments.

FIG. 2 shows a block diagram of a system in accordance with one or moreembodiments.

FIG. 3 shows a block diagram of a system according to one or moreembodiments.

FIGS. 4 and 5 show flowcharts in accordance with one or moreembodiments.

FIG. 6 shows an example in accordance with one or more embodiments.

FIGS. 7.1 and 7.2 show a computing system in accordance with one or moreembodiments.

DETAILED DESCRIPTION

Specific embodiments of the disclosure will now be described in detailwith reference to the accompanying figures. Like elements in the variousfigures are denoted by like reference numerals for consistency.

In the following detailed description of embodiments of the disclosure,numerous specific details are set forth in order to provide a morethorough understanding of the disclosure. However, it will be apparentto one of ordinary skill in the art that the disclosure may be practicedwithout these specific details. In other instances, well-known featureshave not been described in detail to avoid unnecessarily complicatingthe description.

Throughout the application, ordinal numbers (e.g., first, second, third,etc.) may be used as an adjective for an element (i.e., any noun in theapplication). The use of ordinal numbers is not to imply or create anyparticular ordering of the elements nor to limit any element to beingonly a single element unless expressly disclosed, such as using theterms “before”, “after”, “single”, and other such terminology. Rather,the use of ordinal numbers is to distinguish between the elements. Byway of an example, a first element is distinct from a second element,and the first element may encompass more than one element and succeed(or precede) the second element in an ordering of elements.

In general, embodiments of the disclosure include systems and methodsfor using a model to predict various operation parameters for a drillingproject. In particular, the operation parameters may include costs,projected timelines, and risks associated with construction and/oroperating a well for a potential drilling location. For example,multiple data sources are accessed and analyzed regarding rig operationdata involving past drilling operations proximate to the potentialdrilling location or using similar well designs. A remote server mayfilter the rig operation data for use with a model to simulate theconstruction and/or operation of the well at the potential drillinglocation. The model may use one or more artificial intelligencealgorithms to analyze the filtered rig operation data as well asgenerate the operation parameters at the potential drilling location.Accordingly, the remote server may collect well input parameters andlocation information from a user device for the potential drillinglocation and in response transmit a report back to the user devicedescribing the operation parameters. For example, the report may be arisk proposal message that details the amount of time, cost, and risksassociated with building and/or operating one or more wells at thepotential drilling location.

Turning to FIG. 1, FIG. 1 shows a block diagram of a system inaccordance with one or more embodiments. FIG. 1 shows a drilling system(10) according to one or more embodiments. Drill string (58) is shownwithin borehole (46). Borehole (46) may be located in the earth (40)having a surface (42). Borehole (46) is shown being cut by the action ofdrill bit (54). Drill bit (54) may be disposed at the far end of thebottom hole assembly (56) that is attached to and forms the lowerportion of drill string (58). Bottom hole assembly (56) may include anumber of devices including various subassemblies.Measurement-while-drilling (MWD) subassemblies may be included insubassemblies (62). Examples of MWD measurements may include direction,inclination, survey data, downhole pressure (inside the drill pipe,and/or outside and/or annular pressure), resistivity, density, andporosity. Subassemblies (62) may also include a subassembly formeasuring torque and weight on the drill bit (54). The signals from thesubassemblies (62) may be processed in a processor (66). Afterprocessing, the information from processor (66) may be communicated topulser assembly (64). Pulser assembly (64) may convert the informationfrom the processor (66) into pressure pulses in the drilling fluid. Thepressure pulses may be generated in a particular pattern whichrepresents the data from the subassemblies (62). The pressure pulses maytravel upwards though the drilling fluid in the central opening in thedrill string and towards the surface system. The subassemblies in thebottom hole assembly (56) may further include a turbine or motor forproviding power for rotating and steering drill bit (54).

The drilling rig (12) may include a derrick (68) and hoisting system, arotating system, and/or a mud circulation system, for example. Thehoisting system may suspend the drill string (58) and may include drawworks (70), fast line (71), crown block (75), drilling line (79),traveling block and hook (72), swivel (74), and/or deadline (77). Therotating system may include a kelly (76), a rotary table (88), and/orengines (not shown). The rotating system may impart a rotational forceon the drill string (58). Likewise, the embodiments shown in FIG. 1 maybe applicable to top drive drilling arrangements as well. Although thedrilling system (10) is shown being on land, those of skill in the artwill recognize that the described embodiments are equally applicable tomarine environments as well.

The mud circulation system may pump drilling fluid down an opening inthe drill string. The drilling fluid may be called mud, which may be amixture of water and/or diesel fuel, special clays, and/or otherchemicals. The mud may be stored in mud pit (78). The mud may be drawninto mud pumps (not shown), which may pump the mud though stand pipe(86) and into the kelly (76) through swivel (74), which may include arotating seal. Likewise, the described technologies may also beapplicable to underbalanced drilling. If underbalanced drilling is used,at some point prior to entering the drill string, gas may be introducedinto the mud using an injection system (not shown).

The mud may pass through drill string (58) and through drill bit (54).As the teeth of the drill bit (54) grind and gouge the earth formationinto cuttings, the mud may be ejected out of openings or nozzles in thedrill bit (54). These jets of mud may lift the cuttings off the bottomof the hole and away from the drill bit (54), and up towards the surfacein the annular space between drill string (58) and the wall of borehole(46).

At the surface, the mud and cuttings may leave the well through a sideoutlet in blowout preventer (99) and through mud return line (notshown). Blowout preventer (99) comprises a pressure control device and arotary seal. The mud return line may feed the mud into one or moreseparator (not shown) which may separate the mud from the cuttings. Fromthe separator, the mud may be returned to mud pit (78) for storage andre-use.

Various sensors may be placed on the drilling rig (12) to takemeasurements of the drilling equipment. In particular, a hookload may bemeasured by hookload sensor (94) mounted on deadline (77), blockposition and the related block velocity may be measured by a blocksensor (95) which may be part of the draw works (70). Surface torque maybe measured by a sensor on the rotary table (88). Standpipe pressure maybe measured by pressure sensor (92), located on standpipe (86). Signalsfrom these measurements may be communicated to a surface processor (96)or other network elements (not shown) disposed around the drilling rig(12). In addition, mud pulses traveling up the drillstring may bedetected by pressure sensor (92). For example, pressure sensor (92) mayinclude a transducer that converts the mud pressure into electronicsignals. The pressure sensor (92) may be connected to surface processor(96) that converts the signal from the pressure signal into digitalform, stores and demodulates the digital signal into useable MWD data.According to various embodiments described above, surface processor (96)may be programmed to automatically detect one or more rig states basedon the various input channels described. Processor (96) may beprogrammed, for example, to carry out an automated event detection asdescribed above. Processor (96) may transmit a particular rig stateand/or event detection information to user interface system (97) whichmay be designed to warn various drilling personnel of events occurringon the rig and suggest activity to the drilling personnel to avoidspecific events.

Turning to FIG. 2, FIG. 2 shows a block diagram of a system inaccordance with one or more embodiments. As shown in FIG. 2, a cloudserver (e.g., cloud server (210)) is coupled over a network to variousdrilling rigs (e.g., drilling rig A (211), drilling rig B (212)),various drilling management networks (e.g., drilling management networkA (231), drilling management network B (232)), and/or various userdevices (e.g., user device (290)). A cloud server may be a remote serverthat includes hardware and/or software with functionality forcommunicating across a network, such as the Internet. The cloud servermay include functionality for automatically obtaining rig operation data(e.g., rig operation data A (271), rig operation data B (272)) fromvarious drilling rigs and/or drilling management networks. In one ormore embodiments, the cloud server (210) may be similar to the computersystem (700) described in FIGS. 7.1 and 7.2, and the accompanyingdescription. Drilling management networks (231, 232) may be similar todrilling management network (330) described below in FIG. 3 and theaccompanying description.

In some embodiments, a cloud server includes functionality to obtain rigoperation data (e.g., rig operation data A (271), rig operation data B(272)) from various drilling rigs (e.g., drilling rig A (211), drillingrig B (212)) and/or drilling management networks (e.g., drillingmanagement network A (231), drilling management network B (232)). In oneor more embodiments, rig operation data include financial costs and/oramounts of time associated with a drilling operation at a particularrig. In some embodiments, rig operation data may include informationrelated to the equipment used for drilling or constructing the well,including both surface and downhole equipment or any other equipmentused in the rig operations. Likewise, rig operation data may includeinformation relating to sensor data and other measurements regardingsuch drilling equipment, drilling operations, maintenance operations,and/or other operations performed around a drilling rig. In someembodiments, rig operation data include periodic drilling reports fromdrilling sites and data from publically-available databases. In otherembodiments, rig operation data includes interpolated and/orextrapolated data for a desired drilling location based on other rigoperation data, such as legacy data. Moreover, for example, the cloudserver (210) may obtain rig operation data in real time from thedrilling management networks (231, 232) or at periodic intervals, suchas daily, weekly, monthly, etc. A cloud server may store filtered and/orunfiltered rig operation data in a local database (e.g., drillingdatabase (225)).

In some embodiments, a cloud server includes a risk prediction manager(e.g., risk prediction manager (220)). A risk prediction manager may behardware and/or software that includes functionality for determining oneor more operation parameters (e.g., operation parameters (223)). Forexample, operation parameters may output data that project individual oraggregate costs for performing one or more rig operations, such asdrilling operations, at a hypothetical drilling rig at a desiredgeographic location. Likewise, an operation parameter may also specifyan amount of time to complete a particular operation. Operationparameters may also include range of values as well as a probabilitydistribution that one of the values occurs. Examples of operationparameters may include an amount of nonproductive time at a drillingrig, a cost of constructing the drilling rig (including for example theequipment used in the rig operations) at a particular location, anamount of time to complete construction of the drilling rig, and/or acost of drilling a wellbore according to various well input parameters.Well input parameters may include a wellbore radius, a length of thewellbore, and/or other design parameters for the wellbore.

In some embodiments, the cloud server includes a model (e.g., model A(215)) that include hardware and/or software with functionality forpredicting one or more operation parameters (e.g., operation parameters(223)). For example, the model A (215) may include various datavirtualization tools, search and knowledge discovery tools, streamanalytics tools, relational databases, NoSQL databases, and variousapplications for generating outputs based on location information, wellinput parameters, rig operation data, and other information. In someembodiments, a model include functionality for determining operationparameters based on one or more artificial intelligence algorithms. Forexample, artificial intelligence algorithms may include decision treealgorithms, one or more support vector machines, an ensemble method,and/or a naïve Bayes classifier algorithm. In some embodiments, themodel identifies a level of risk associated with various rig operations,e.g., one or more probabilities that a rig operation is completedwithout nonproductive time or below a threshold amount of nonproductivetime.

Further, the risk prediction manager (220) may determine various costsand/or an amount of time for constructing and operating a drilling rigat a particular location, e.g., a desired geographic location submittedin a request for a proposal. As such, the risk prediction manager (220)may generate a total estimate for constructing a well as well asestimates for various rig operations performed at the drilling rig. Anestimate may be included in a risk proposal message (e.g., risk proposalmessage (273)), for example. Accordingly, the risk prediction manager(220) may transmit a risk proposal message to a user device (e.g., userdevice (290)) automatically or in response to a request obtained fromthe user device. For example, a user may select a desired geographiclocation for a potential drilling site with the user device and transmitvarious well input parameters for the drilling site to a cloud server.In response, the cloud server may generate a risk proposal message andtransmit the message back to the user device.

User devices (e.g., user device (290)) may include hardware and/orsoftware for receiving inputs from a user and/or providing outputs to auser. Moreover, a user device may be coupled to a drilling managementnetwork and/or a cloud server. For example, user devices may includefunctionality for presenting data and/or receiving inputs from a userregarding various drilling operations and/or maintenance operationsperformed within a drilling management network. Examples of user devicesmay include personal computers, smartphones, human machine interfaces,and any other devices coupled to a network that obtain inputs from oneor more users, e.g., by providing a graphical user interface (GUI).Likewise, a user device may present data and/or receive control commandsfrom a user for operating a drilling rig.

Turning to FIG. 3, FIG. 3 shows a block diagram of a system inaccordance with one or more embodiments. As shown in FIG. 3, a drillingmanagement network (330) may include a human machine interface (HMI)(e.g., HMI (333)), a historian (e.g., historian (334)), and variousnetwork elements (e.g., network elements (331)). A HMI may be hardwareand/or software coupled to the drilling management network (330). Forexample, the HMI may allow the operator to interact with the drillingsystem, e.g., to send a command to operate an equipment, or to viewsensor information from drilling equipment. The HMI may includefunctionality for presenting data and/or receiving inputs from a userregarding various drilling operations and/or maintenance operations. Forexample, a HMI may include software to provide a graphical userinterface (GUI) for presenting data and/or receiving control commandsfor operating a drilling rig. A network element (e.g., network elements(331)) may refer to various hardware components within a network, suchas switches, routers, hubs or any other logical entities for uniting oneor more physical devices on the network. In particular, a networkelement, the human machine interface, and/or the historian may be acomputing system similar to the computing system (700) described inFIGS. 7.1 and 7.2, and the accompanying description.

In one or more embodiments, a sensor device (e.g., sensor device X(320)) is coupled to the drilling management network (330). Inparticular, a sensor device may include hardware and/or software thatincludes functionality to obtain one or more sensor measurements, e.g.,a sensor measurement of an environment condition proximate the sensordevice. The sensor device may process the sensor measurements intovarious types of sensor data (e.g., sensor data (315)). For example, thesensor device X (320) may include functionality to convert sensormeasurements obtained from sensor circuitry (e.g., sensor circuitry(324)) into a communication protocol format that may be transmitted overthe drilling management network (330) by a communication interface(e.g., communication interface (322)). Sensor devices may includepressure sensors, torque sensors, rotary switches, weight sensors,position sensors, microswitches, etc. The sensor device may includesmart sensors. In some embodiments, sensor devices include sensorcircuitry without a communication interface or memory. For example, asensor device may be coupled with a computer device that transmitssensor data over a drilling management network.

Moreover, a sensor device may include a processor (e.g., processor(321)), a communication interface (e.g., communication interface (322)),memory (e.g., memory (323)), and sensor circuitry (e.g., sensorcircuitry (324)). The processor may be similar to the computer processor(702) described below in FIG. 7.1 and the accompanying description. Thecommunication interface (322) may be similar to the communicationinterface (712) describe below in FIG. 7.1 and the accompanyingdescription. The memory (323) may be similar to the non-persistentstorage (704) and/or the persistent storage (706) described below inFIG. 7.1 and the accompanying description. The sensor circuitry (324)may be similar to various sensors (e.g., hookload sensor (94), blocksensor (95), pressure sensor (92), etc.) described in FIG. 1 and theaccompanying description.

In one or more embodiments, a drilling management network may includedrilling equipment (e.g., drilling equipment (332)) such as draw works(60), top drive, mud pumps and other components described above in FIG.1 and the accompanying description). The drilling management network(330) may further include various drilling operation control systems(e.g., drilling operation control systems (335)) and various maintenancecontrol systems (e.g., maintenance control systems (336)). Drillingoperation control systems and/or maintenance control systems mayinclude, for example, programmable logic controllers (PLCs) that includehardware and/or software with functionality to control one or moreprocesses performed by a drilling rig, including, but not limited to thecomponents described in FIG. 1. Specifically, a programmable logiccontroller may control valve states, fluid levels, pipe pressures,warning alarms, and/or pressure releases throughout a drilling rig. Inparticular, a programmable logic controller may be a ruggedized computersystem with functionality to withstand vibrations, extreme temperatures,wet conditions, and/or dusty conditions, for example, around a drillingrig. Without loss of generality, the term “control system” may refer toa drilling operation control system that is used to operate and controlthe equipment, a drilling data acquisition and monitoring system that isused to acquire drilling process and equipment data and to monitor theoperation of the drilling process, or a drilling interpretation softwaresystem that is used to analyze and understand drilling events andprogress.

Moreover, drilling operation control systems and/or maintenance controlsystems may refer to control systems that include multiple PLCs withinthe drilling management network (330). For example, a control system mayinclude functionality to control operations within a system, assembly,and/or subassembly described above in FIG. 1 and the accompanyingdescription. As such, one or more of the drilling operation controlsystems (335) may include functionality to monitor and/or performvarious drilling processes with respect to the mud circulation system,the rotating system, the hoisting system, a pipe handling system, and/orvarious other drilling activities described with respect to FIG. 1 andthe accompanying description. Likewise, one or more of the maintenancecontrol systems (336) may include functionality to monitor and/orperform various maintenance activities regarding drilling equipmentlocated around a drilling rig. While drilling operation control systemsand maintenance control systems are shown as separate devices in FIG. 3,in one or more embodiments, a programmable logic controller and otherdrilling equipment (332) on a drilling rig may be used in a drillingoperation control system and a maintenance control system at the sametime.

In one or more embodiments, a sensor device includes functionality toestablish a network connection (e.g., network connection (340)) with oneor more devices and/or systems (e.g., cloud server (210), drillingoperation control systems (335), maintenance control systems (336)) on adrilling management network. In one or more embodiments, for example,the network connection (340) may be an Ethernet connection thatestablishes an Internet Protocol (IP) address for the sensor device X(320). Accordingly, one or more devices and/or systems on the drillingmanagement network (330) may transmit data packets to the sensor deviceX (320) and/or receive data packets from the sensor device X (320) usingthe Ethernet network protocol. For example, sensor data (e.g., sensordata (315)) may be sent over the drilling management network (330) indata packets using a communication protocol. Sensor data may includesensor measurements, processed sensor data based on one or moreunderlying sensor measurements or parameters, metadata regarding asensor device such as timestamps and sensor device identificationinformation, content attributes, sensor configuration information suchas offset, conversion factors, etc. As such, the sensor device X (320)may act as a host device on the drilling management network (330), e.g.as a network node and/or an endpoint on the drilling management network(330). In one embodiment, one or more sensors may connect to thedrilling management network through a power-over-Ethernet network.

In some embodiments, a drilling management network may collect rigoperation data from sensors, control systems, and/or other networkdevices around a drilling rig. After collection, the drilling managementnetwork may then transmit the rig operation data to a remote serversimilar to the cloud server (210) described above in FIG. 2 and theaccompanying description. Likewise, a cloud server may transmit arequest to one or more network devices on a drilling management networkfor particular rig operation data. Thus, drilling management networksmay include functionality for automating the data gathering process forcollecting rig operation data in order to update drilling databases on acloud server periodically or in real time.

While FIGS. 1, 2, and 3 show various configurations of components, otherconfigurations may be used without departing from the scope of thedisclosure. For example, various components in FIGS. 1, 2, and 3 may becombined to create a single component. As another example, thefunctionality performed by a single component may be performed by two ormore components.

Turning to FIG. 4, FIG. 4 shows a flowchart in accordance with one ormore embodiments. Specifically, FIG. 4 describes a general method forsimulating rig operations at a desired geographic location. One or moreblocks in FIG. 4 may be performed by one or more components (e.g., riskprediction manager (220)) as described in FIGS. 1, 2, and/or 3. Whilethe various blocks in FIG. 4 are presented and described sequentially,one of ordinary skill in the art will appreciate that some or all of theblocks may be executed in different orders, may be combined or omitted,and some or all of the blocks may be executed in parallel. Furthermore,the blocks may be performed actively or passively.

In Block 400, geographic location data is obtained regarding a desiredgeographic location for a drilling rig in accordance with one or moreembodiments. For example, geographic location data may correspond toglobal positioning system (GPS) coordinates or other informationidentifying a geographic region of interest. The geographic locationinformation may also include a radius defining a coverage area of thedesired geographic location, e.g., as multiple drilling sites may beavailable for one or more drilling rigs. Moreover, the desiredgeographic location may correspond to one or more potential drillinglocations in a particular geological region, e.g., Permian basin, Bakkenformation, etc.

In Block 410, rig operation data is obtained regarding various drillingrigs at different geographic locations in accordance with one or moreembodiments. In some embodiments, a cloud server gathers informationfrom various data sources, e.g., detailed daily drilling reports (DDR)from multiple drilling rigs, external drilling databases, etc. A riskprediction manager may parse data from different data sources to extractrig operation data corresponding to one or more predeterminedattributes. Examples of rig operation data may include an amount ofdrilling time for a particular drilling operation as well as otherattributes of the drilling operation. For example, the rig operationdata may describe the drilling operation based on a hole size of awellbore, a predetermined interval that the wellbore is drilled, a typeof basin being drilled by the wellbore, a type of petroleum playcomprising the wellbore, a drill string design (including but notlimited to the bottomhole assembly), a casing and completion design forthe wellbore, and surface equipment needed for the operation. Rigoperation data may also include historical data at a drilling rig, suchas clean time (i.e., well construction time without nonproductive time),nonproductive time (NOT) at the drilling rig, and/or the identifiedcauses relating to the nonproductive time.

In some embodiments, the rig operation data includes data regardingvarious sequential drilling operations for constructing a wellbore. Forexample, rig operation data may include total costs, completion time fora wellbore, etc., which may be more comprehensive than data directed toa singular phase or equipment (such as a BHA, completion design, etc.)in a well construction process. Thus, the sequential drilling operationsare intended to encompass different pieces of equipment as drillingprogresses from one phase to another, while also being comprehensive ateach drilling phase. Accordingly, the rig operation data may be anaggregation of data regarding different phases or milestones involved indrilling and/or completing an entire wellbore at one or more drillinglocations. Thus, the rig operation data may provide an overview of theentire costs and risks associated with drilling a potential well.

Furthermore, the rig operation data may be stored in a database on acloud server or in another remote location. In some embodiments, a riskprediction manager may use rig operation data from different locationsto generate rig operation data for an unexplored region. For example,synthetic rig operation data may be generated using one or moreartificial intelligence algorithms with respect to a well profile andlocation using data from a similar well profile and location. Rigoperation data may also be obtained from various sensor devices disposedaround a drilling management network. In particular, the sensor devicesmay be similar to the sensor device X (320) described in FIG. 3 and theaccompanying description. In one or more embodiments, the sensor deviceconnects directly to a risk prediction manager. In some embodiments,various control systems in a drilling management network may provide rigoperation data directly to the risk prediction manager.

In Block 420, a model is generated using rig operation data inaccordance with one or more embodiments. Specifically, rig operationdata may be filtered to produce a sparse dataset, e.g., a dataset thatis smaller and more manageable than the data in a drilling database.After filtering, a model may analyze the dataset using geographiclocation data and well input parameters. For example, the geographiclocation data may correspond to one or more physical dimensions definingone or more sites for a proposed wellbore, where the locationinformation may narrow the area of focus for the model. Likewise, themodel may also obtain various well input parameters of a proposedwellbore design. The model may be similar to the model A (215) describedabove in FIG. 2 and the accompanying description.

In Block 430, a sequence of rig operations for constructing a portion ofa wellbore at a desired geographic location are simulated usinggeographic location data and a model in accordance with one or moreembodiments. For example, the simulations may be performed prior toconstruction of a wellbore in order to determine various possible risksassociated with the construction. In some embodiments, a risk predictionmanager may perform various Monte Carlo simulations using a model. Forexample, Monte Carlo simulations may generate various possible outcomescorresponding to one or more operation parameters within the sequence ofrig operations, such as amounts of clean time, various degrees of riskin well construction and/or operating the drilling, and ranges of costassociated with the drilling rig. Moreover, the sequence of rigoperations may include specific drilling operations based on aparticular well design, e.g., simulating a drilling path for a welldesign defined by predetermined well input parameters. In someembodiments, the sequence of rig operations include completing thewellbore, surface operations, and downhole operations.

Turning to FIG. 5, FIG. 5 shows a flowchart in accordance with one ormore embodiments. Specifically, FIG. 5 describes a general method forusing a model for predicting operation parameters of a sequence of rigoperations. One or more blocks in FIG. 5 may be performed by one or morecomponents (e.g., risk prediction manager (220)) as described in FIGS.1, 2, and/or 3. While the various blocks in FIG. 5 are presented anddescribed sequentially, one of ordinary skill in the art will appreciatethat some or all of the blocks may be executed in different orders, maybe combined or omitted, and some or all of the blocks may be executed inparallel. Furthermore, the blocks may be performed actively orpassively.

In Block 500, geographic location data is obtained regarding a desiredgeographic location for a drilling rig in accordance with one or moreembodiments. Block 500 may be similar to Block 400 described above inFIG. 4 and the accompanying description. In some embodiments, thegeographic location data may be part of a request for a risk proposalmessage transmitted by a user device to a cloud server. For example, auser may request a risk proposal message regarding a desired geographiclocation for a drilling rig. Accordingly, a user device may transmit thegeographic location data with various well input parameters to a riskprediction manager on a cloud server.

In Block 510, rig operation data is obtained from a drilling database inaccordance with one or more embodiments. Block 510 may be similar toBlock 410 described above in FIG. 4 and the accompanying description.The drilling database may be similar to drilling database (225)described above in FIG. 2 and the accompanying description.

In Block 520, a regression analysis is performed on rig operation datausing various well input parameters in accordance with one or moreembodiments. In some embodiments, multiple regression analyses areperformed on rig operation data with a dependency on time and,separately, on cost to filter the rig operation data into a particulardataset. For example, independent variables for performing a timeanalysis may include various drilling operation information such as ahole size at a wellbore, a drilling interval at the wellbore, etc.Independent variables for cost estimation may include a type of basinand a type of petroleum play to be drilled, casing design for awellbore, etc.

In particular, a regression analysis may provide an estimated range ofclean time and cost based on various well input parameters for awellbore. The well input parameters may be provided with the geographiclocation data in Block 500, for example. Clean time and nonproductivetime within the rig operation data may be used by a risk predictionmanager to verify a degree of precision of the outputs of a model. Assuch, the results of one or more regression analyses may be comparedwith user data to determine a likelihood of accuracy. In someembodiments, a regression analysis generates a distribution for variousrig operation data values that considers the frequency and effect onother rig operation values.

In Block 530, a sequence of rig operations are simulated using a modeland a regression analysis at a desired geographic location in accordancewith one or more embodiments. The simulation may be similar to thesimulation performed in Block 430 described above in FIG. 4 and theaccompanying description.

In some embodiments, rig operations undergo multiple simulations usingupdated rig operation data, geographic location data, and/or an updatedmodel. As such, Block 530 may be performed iteratively with or withoutrepeating a regression analysis. Likewise, rig operations may beresimulated using the same or different rig operation data. Thus, theamount of time to obtain operation parameters for a drilling rig at adesired geographic location may be reduced accordingly by removing oneor more blocks during an additional simulation.

In Block 540, a risk proposal message is transmitted to a user devicebased on one or more simulations of a sequence of rig operations inaccordance with one or more embodiments. In some embodiments, a riskproposal message is generated that may describe a total cost of adrilling project, various itemized costs of the drilling project, atotal amount of time for completing the drilling project, variousindividual times for completing different milestones relating to thedrilling project, etc. A risk prediction manager may automaticallygenerate a risk proposal message using various simulations performed ata desired geographical location in response to a request from a userdevice. Once generated, the risk prediction manager may transmit therisk proposal message to a user device where the message is presented ona display of the user device.

Moreover, the risk proposal message may include various risks of adrilling project determined by performing simulations at a desiredgeographic location. In particular, a risk prediction manager may usethe model to determine various risk probabilities regarding costs andamounts of clean time and/or nonproductive time. For example, the riskproposal message may describe the probability that a drilling projectwill exceed a proposed bid. Thus, the risk proposal message may be usedin preparation of submitting a bid for a drilling project. Likewise,multiple risk proposal messages may be generated for different wellinput parameters and desired geographic locations, e.g., as part of anegotiation process for a commercial contrast for a drilling project.

Turning to FIG. 6, FIG. 6 provides an example of a risk proposalmessage. The following example is for explanatory purposes only and notintended to limit the scope of the disclosed technology.

Turning to FIG. 6, FIG. 6 shows a risk proposal message (685) for aPermian Basin Drilling Rig X. In particular, the risk proposal message(685) describes various rig operation attributes (i.e., locationattribute (610), clean time attribute (620), drilling rig nonproductivetime attribute (630), single well total construction time attribute(640), risk attribute (650), activity-based risk profile attribute(655), cost-of-completing well attribute (660), and a bid submissionattribute (670)). For the location attribute (610), the value of therespective predicted operation parameter is GPS Coordinates (X, Y)(611). For the clean time attribute (620), the value of the respectivepredicted operation parameter is 80% (621), where 80% of theconstruction time may be directed to constructing a drilling rig anddrilling a wellbore. For the drilling rig nonproductive time attribute(630), the value of the respective predicted operation parameter is arange of values (631) from 15% to 19% of the construction time beingdirected to nonproductive time. For the single well total constructiontime attribute (640), the value of the respective predicted operationparameter is a range of values (641) from 14 to 18 days to complete thewellbore. For the risk attribute (650), the value of the predictedoperation parameter is a 5% chance (651) that the construction of thewellbore exceeds the value of the bid submission attribute (670). Forthe activity-based risk profile attribute (655), the value of thepredicted operation parameter is a 60% chance (656) that theconstruction of the wellbore will result in fluid losses, and the valueof the predicted operation parameter is a 40% chance (656) that theconstruction of the wellbore results in a stuck pipe. In a stuck pipe,the pipe cannot be freed from the hole without being damaged, andwithout exceeding the drilling rig's maximum allowed hook load. For thecost-of-completing well attribute (660), the respective value of thepredicted operation parameter is a range of values (661) from $700,000to $1,200,000. For the bid submission attribute (670), the respectivevalue of the predicted operation parameter is $1,100,000 (671).

Keeping with FIG. 6, the risk proposal message (685) may be shown in agraphical user interface of a user device (not shown). Likewise, a usermay provide various inputs to the user device to modify one or more ofthe attribute values (611, 621, 631, 641, 651, 656, 661, 671). A riskprediction manager (not shown) may then automatically update the otherattributes accordingly. For example, based on the value of the singlewell total construction time attribute (640), a user may adjust thevalue of the location attribute (610) to decrease the number of days tocomplete the well.

Returning to FIG. 5, in Block 550, a model is updated using rigoperation data and one or more artificial intelligence methods inaccordance with one or more embodiments. In some embodiments, the modelis updated periodically as rig operation data is further collected fromvarious data sources. Further, one or more artificial intelligencealgorithms may adjust the model based on comparing the model's operationparameters and actual rig operation data. For example, a risk predictionmanager may adjust the model iteratively using a search method until thedifference between predicted operation parameters and the actual rigoperation data satisfies a predetermined criterion. For example, thepredetermined criterion may be convergence of the operation parametersat a local or global minimum. Various search methods may be used toadjust the model, such as a gradient descent, a Newton-Raphson method,and various other types of search methods.

With respect to the embodiments discussed above in FIGS. 4 and 5,various procedures are automated as to provide faster delivery time incomparison to manual methods for preparing and transmitting drillingproject proposals. For example, a proposal produced using offset wellanalysis may take a month to assess time, cost, and risks of theproposal. Before even receiving user data for well time estimation,business development discussions and a signed non-disclosure agreementmay be required. Likewise, the data analytics described above mayprovide a proactive approach to identifying potential drilling projectsbased upon: performance, permits, and risk. Likewise, automating theprocedures for risk size identification from multiple data sourcesimproves consistency for risk assignment among drilling projects.Moreover, by performing an iterative quality check between the outputsof the prediction model and user data, issues in risk proposal messagesmay be identified prior to formal bid submissions.

Embodiments may be implemented on a computing system. Any combination ofmobile, desktop, server, router, switch, embedded device, or other typesof hardware may be used. For example, as shown in FIG. 7.1, thecomputing system (700) may include one or more computer processors(702), non-persistent storage (704) (e.g., volatile memory, such asrandom access memory (RAM), cache memory), persistent storage (706)(e.g., a hard disk, an optical drive such as a compact disk (CD) driveor digital versatile disk (DVD) drive, a flash memory, etc.), acommunication interface (712) (e.g., Bluetooth interface, infraredinterface, network interface, optical interface, etc.), and numerousother elements and functionalities.

The computer processor(s) (702) may be an integrated circuit forprocessing instructions. For example, the computer processor(s) may beone or more cores or micro-cores of a processor. The computing system(700) may also include one or more input devices (710), such as atouchscreen, keyboard, mouse, microphone, touchpad, electronic pen, orany other type of input device.

The communication interface (712) may include an integrated circuit forconnecting the computing system (700) to a network (not shown) (e.g., alocal area network (LAN), a wide area network (WAN) such as theInternet, mobile network, or any other type of network) and/or toanother device, such as another computing device.

Further, the computing system (700) may include one or more outputdevices (708), such as a screen (e.g., a liquid crystal display (LCD), aplasma display, touchscreen, cathode ray tube (CRT) monitor, projector,or other display device), a printer, external storage, or any otheroutput device. One or more of the output devices may be the same ordifferent from the input device(s). The input and output device(s) maybe locally or remotely connected to the computer processor(s) (702),non-persistent storage (704), and persistent storage (706). Manydifferent types of computing systems exist, and the aforementioned inputand output device(s) may take other forms.

Software instructions in the form of computer readable program code toperform embodiments of the disclosure may be stored, in whole or inpart, temporarily or permanently, on a non-transitory computer readablemedium such as a CD, DVD, storage device, a diskette, a tape, flashmemory, physical memory, or any other computer readable storage medium.Specifically, the software instructions may correspond to computerreadable program code that, when executed by a processor(s), isconfigured to perform one or more embodiments of the disclosure.

The computing system (700) in FIG. 7.1 may be connected to or be a partof a network. For example, as shown in FIG. 7.2, the network (720) mayinclude multiple nodes (e.g., node X (722), node Y (724)). Each node maycorrespond to a computing system, such as the computing system shown inFIG. 7.1, or a group of nodes combined may correspond to the computingsystem shown in FIG. 7.1. By way of an example, embodiments of thedisclosure may be implemented on a node of a distributed system that isconnected to other nodes. By way of another example, embodiments of thedisclosure may be implemented on a distributed computing system havingmultiple nodes, where each portion of the disclosure may be located on adifferent node within the distributed computing system. Further, one ormore elements of the aforementioned computing system (700) may belocated at a remote location and connected to the other elements over anetwork.

Although not shown in FIG. 7.2, the node may correspond to a blade in aserver chassis that is connected to other nodes via a backplane. By wayof another example, the node may correspond to a server in a datacenter. By way of another example, the node may correspond to a computerprocessor or micro-core of a computer processor with shared memoryand/or resources.

The nodes (e.g., node X (722), node Y (724)) in the network (720) may beconfigured to provide services for a client device (726). For example,the nodes may be part of a cloud computing system. The nodes may includefunctionality to receive requests from the client device (726) andtransmit responses to the client device (726). The client device (726)may be a computing system, such as the computing system shown in FIG.7.1. Further, the client device (726) may include and/or perform all ora portion of one or more embodiments of the disclosure.

The computing system or group of computing systems described in FIGS.7.1 and 7.2 may include functionality to perform a variety of operationsdisclosed herein. For example, the computing system(s) may performcommunication between processes on the same or different systems. Avariety of mechanisms, employing some form of active or passivecommunication, may facilitate the exchange of data between processes onthe same device. Examples representative of these inter-processcommunications include, but are not limited to, the implementation of afile, a signal, a socket, a message queue, a pipeline, a semaphore,shared memory, message passing, and a memory-mapped file. Furtherdetails pertaining to a couple of these non-limiting examples areprovided below.

Based on the client-server networking model, sockets may serve asinterfaces or communication channel end-points enabling bidirectionaldata transfer between processes on the same device. Foremost, followingthe client-server networking model, a server process (e.g., a processthat provides data) may create a first socket object. Next, the serverprocess binds the first socket object, thereby associating the firstsocket object with a unique name and/or address. After creating andbinding the first socket object, the server process then waits andlistens for incoming connection requests from one or more clientprocesses (e.g., processes that seek data). At this point, when a clientprocess wishes to obtain data from a server process, the client processstarts by creating a second socket object. The client process thenproceeds to generate a connection request that includes at least thesecond socket object and the unique name and/or address associated withthe first socket object. The client process then transmits theconnection request to the server process. Depending on availability, theserver process may accept the connection request, establishing acommunication channel with the client process, or the server process,busy in handling other operations, may queue the connection request in abuffer until the server process is ready. An established connectioninforms the client process that communications may commence. Inresponse, the client process may generate a data request specifying thedata that the client process wishes to obtain. The data request issubsequently transmitted to the server process. Upon receiving the datarequest, the server process analyzes the request and gathers therequested data. Finally, the server process then generates a replyincluding at least the requested data and transmits the reply to theclient process. The data may be transferred, more commonly, as datagramsor a stream of characters (e.g., bytes).

Shared memory refers to the allocation of virtual memory space in orderto substantiate a mechanism for which data may be communicated and/oraccessed by multiple processes. In implementing shared memory, aninitializing process first creates a shareable segment in persistent ornon-persistent storage. Post creation, the initializing process thenmounts the shareable segment, subsequently mapping the shareable segmentinto the address space associated with the initializing process.Following the mounting, the initializing process proceeds to identifyand grant access permission to one or more authorized processes that mayalso write and read data to and from the shareable segment. Changes madeto the data in the shareable segment by one process may immediatelyaffect other processes, which are also linked to the shareable segment.Further, when one of the authorized processes accesses the shareablesegment, the shareable segment maps to the address space of thatauthorized process. Often, one authorized process may mount theshareable segment, other than the initializing process, at any giventime.

Other techniques may be used to share data, such as the various datadescribed in the present application, between processes withoutdeparting from the scope of the disclosure. The processes may be part ofthe same or different application and may execute on the same ordifferent computing system.

Rather than or in addition to sharing data between processes, thecomputing system performing one or more embodiments of the disclosuremay include functionality to receive data from a user. For example, inone or more embodiments, a user may submit data via a graphical userinterface (GUI) on the user device. Data may be submitted via thegraphical user interface by a user selecting one or more graphical userinterface widgets or inserting text and other data into graphical userinterface widgets using a touchpad, a keyboard, a mouse, or any otherinput device. In response to selecting a particular item, informationregarding the particular item may be obtained from persistent ornon-persistent storage by the computer processor. Upon selection of theitem by the user, the contents of the obtained data regarding theparticular item may be displayed on the user device in response to theuser's selection.

By way of another example, a request to obtain data regarding theparticular item may be sent to a server operatively connected to theuser device through a network. For example, the user may select auniform resource locator (URL) link within a web client of the userdevice, thereby initiating a Hypertext Transfer Protocol (HTTP) or otherprotocol request being sent to the network host associated with the URL.In response to the request, the server may extract the data regardingthe particular selected item and send the data to the device thatinitiated the request. Once the user device has received the dataregarding the particular item, the contents of the received dataregarding the particular item may be displayed on the user device inresponse to the user's selection. Further to the above example, the datareceived from the server after selecting the URL link may provide a webpage in Hyper Text Markup Language (HTML) that may be rendered by theweb client and displayed on the user device.

Once data is obtained, such as by using techniques described above orfrom storage, the computing system, in performing one or moreembodiments of the disclosure, may extract one or more data items fromthe obtained data. For example, the extraction may be performed asfollows by the computing system (700) in FIG. 7.1. First, the organizingpattern (e.g., grammar, schema, layout) of the data is determined, whichmay be based on one or more of the following: position (e.g., bit orcolumn position, Nth token in a data stream, etc.), attribute (where theattribute is associated with one or more values), or a hierarchical/treestructure (consisting of layers of nodes at different levels ofdetail—such as in nested packet headers or nested document sections).Then, the raw, unprocessed stream of data symbols is parsed, in thecontext of the organizing pattern, into a stream (or layered structure)of tokens (where each token may have an associated token “type”).

Next, extraction criteria are used to extract one or more data itemsfrom the token stream or structure, where the extraction criteria areprocessed according to the organizing pattern to extract one or moretokens (or nodes from a layered structure). For position-based data, thetoken(s) at the position(s) identified by the extraction criteria areextracted. For attribute/value-based data, the token(s) and/or node(s)associated with the attribute(s) satisfying the extraction criteria areextracted. For hierarchical/layered data, the token(s) associated withthe node(s) matching the extraction criteria are extracted. Theextraction criteria may be as simple as an identifier string or may be aquery presented to a structured data repository (where the datarepository may be organized according to a database schema or dataformat, such as XML).

The extracted data may be used for further processing by the computingsystem. For example, the computing system of FIG. 7.1, while performingone or more embodiments of the disclosure, may perform data comparison.Data comparison may be used to compare two or more data values (e.g., A,B). For example, one or more embodiments may determine whether A>B, A=B,A !=B, A<B, etc. The comparison may be performed by submitting A, B, andan opcode specifying an operation related to the comparison into anarithmetic logic unit (ALU) (i.e., circuitry that performs arithmeticand/or bitwise logical operations on the two data values). The ALUoutputs the numerical result of the operation and/or one or more statusflags related to the numerical result. For example, the status flags mayindicate whether the numerical result is a positive number, a negativenumber, zero, etc. By selecting the proper opcode and then reading thenumerical results and/or status flags, the comparison may be executed.For example, in order to determine if A>B, B may be subtracted from A(i.e., A−B), and the status flags may be read to determine if the resultis positive (i.e., if A>B, then A−B>0). In one or more embodiments, Bmay be considered a threshold, and A is deemed to satisfy the thresholdif A=B or if A>B, as determined using the ALU. In one or moreembodiments of the disclosure, A and B may be vectors, and comparing Awith B includes comparing the first element of vector A with the firstelement of vector B, the second element of vector A with the secondelement of vector B, etc. In one or more embodiments, if A and B arestrings, the binary values of the strings may be compared.

The computing system in FIG. 7.1 may implement and/or be connected to adata repository. For example, one type of data repository is a database.A database is a collection of information configured for ease of dataretrieval, modification, re-organization, and deletion. DatabaseManagement System (DBMS) is a software application that provides aninterface for users to define, create, query, update, or administerdatabases.

The user, or software application, may submit a statement or query intothe DBMS. Then the DBMS interprets the statement. The statement may be aselect statement to request information, update statement, createstatement, delete statement, etc. Moreover, the statement may includeparameters that specify data, or data container (database, table,record, column, view, etc.), identifier(s), conditions (comparisonoperators), functions (e.g. join, full join, count, average, etc.), sort(e.g. ascending, descending), or others. The DBMS may execute thestatement. For example, the DBMS may access a memory buffer, a referenceor index a file for read, write, deletion, or any combination thereof,for responding to the statement. The DBMS may load the data frompersistent or non-persistent storage and perform computations to respondto the query. The DBMS may return the result(s) to the user or softwareapplication.

The computing system of FIG. 7.1 may include functionality to presentraw and/or processed data, such as results of comparisons and otherprocessing. For example, presenting data may be accomplished throughvarious presenting methods. Specifically, data may be presented througha user interface provided by a computing device. The user interface mayinclude a GUI that displays information on a display device, such as acomputer monitor or a touchscreen on a handheld computer device. The GUImay include various GUI widgets that organize what data is shown as wellas how data is presented to a user. Furthermore, the GUI may presentdata directly to the user, e.g., data presented as actual data valuesthrough text, or rendered by the computing device into a visualrepresentation of the data, such as through visualizing a data model.

For example, a GUI may first obtain a notification from a softwareapplication requesting that a particular data object be presented withinthe GUI. Next, the GUI may determine a data object type associated withthe particular data object, e.g., by obtaining data from a dataattribute within the data object that identifies the data object type.Then, the GUI may determine any rules designated for displaying thatdata object type, e.g., rules specified by a software framework for adata object class or according to any local parameters defined by theGUI for presenting that data object type. Finally, the GUI may obtaindata values from the particular data object and render a visualrepresentation of the data values within a display device according tothe designated rules for that data object type.

Data may also be presented through various audio methods. In particular,data may be rendered into an audio format and presented as sound throughone or more speakers operably connected to a computing device.

Data may also be presented to a user through haptic methods. Forexample, haptic methods may include vibrations or other physical signalsgenerated by the computing system. For example, data may be presented toa user using a vibration generated by a handheld computer device with apredefined duration and intensity of the vibration to communicate thedata.

The above description of functions presents only a few examples offunctions performed by the computing system of FIG. 7.1 and the nodesand/or client device in FIG. 7.2. Other functions may be performed usingone or more embodiments of the disclosure.

While the disclosure has been described with respect to a limited numberof embodiments, those skilled in the art, having benefit of thisdisclosure, will appreciate that other embodiments can be devised whichdo not depart from the scope of the disclosure as disclosed herein.Accordingly, the scope of the disclosure should be limited only by theattached claims.

What is claimed is:
 1. A method, comprising: obtaining, by a computerprocessor, geographic location data regarding a desired geographiclocation for a first drilling rig; obtaining, by the computer processor,rig operation data regarding a plurality of drilling rigs at differentgeographic locations; performing one or more regression analyses on therig operation data with a dependency on time, cost, or a combinationthereof so as to filter the rig operation data and generate an estimatedrange of clean time and cost; generating, using the rig operation data,a model that identifies a level of risk associated with a plurality ofrig operations; simulating, by the computer processor and using thegeographic location data and the model, a sequence of rig operations forconstructing a portion of a wellbore drilled by the first drilling rigat the desired geographic location based at least in part on the modeland the one or more regression analyses; adjusting the model, using anartificial intelligence algorithm, based on a comparison of operationparameters of the model and the rig operation data; determining aprecision of the model based on a comparison of the estimated range ofclean time and non-productive time with a measured clean time and ameasured non-productive time, respectively; and generating a controlsignal to operate an equipment based at least in part on the results ofthe simulating, wherein operation of the equipment changes in responseto receiving the control signal.
 2. The method of claim 1, wherein thesequence of rig operations are simulated prior to construction of thewellbore at the desired geographic location, and wherein the sequence ofrig operation comprises surface operations, and downhole operations. 3.The method of claim 1, wherein the model further identifies a total costand amount of time associated with performing a predetermined drillingoperation for constructing the portion of the wellbore.
 4. The method ofclaim 1, wherein the rig operation data comprises an amount of drillingtime for a predetermined drilling operation based on a hole size of awellbore, a predetermined interval that the wellbore is drilled, a typeof basin being drilled by the wellbore, a type of petroleum playcomprising the wellbore, and a casing design for the wellbore, andwherein the rig operation data further comprises a cost of thepredetermined drilling operation.
 5. The method of claim 1, furthercomprising: obtaining a plurality of well input parameters forconstructing the wellbore; and wherein the one or more regressionanalyses on the rig operation data are performed based on the pluralityof well input parameters.
 6. The method of claim 1, wherein the cleancost comprises a total amount of time minus an amount of nonproductivetime for constructing the wellbore, and wherein the cost comprises acost of constructing the wellbore at the desired geographic location, acost of operating the first drilling rig at the desired geographiclocation, or both.
 7. The method of claim 1, wherein generating themodel comprises: obtaining the model; and updating the model in realtime based on the rig operation data, wherein the model is updatediteratively using a search method until the model converges to apredetermined criterion.
 8. The method of claim 1, wherein simulatingthe sequence of rig operations comprises performing one or more MonteCarlo simulations for constructing the portion of the wellbore.
 9. Themethod of claim 1, wherein generating the model comprises using anartificial intelligence algorithm on the rig operation data, and whereinthe artificial intelligence algorithm is selected from a groupconsisting of a decision tree algorithm, a support vector machine, anensemble method, and a naive Bayes classifier algorithm.
 10. The methodof claim 1, wherein obtaining the rig operation data comprises:establishing, at a remote server, a first network connection to adrilling management network at a second drilling rig, wherein thedrilling management network is configured to operate automatically aplurality of control systems at the second drilling rig, and wherein therig operation data is obtained from the drilling management network. 11.The method of claim 1, wherein simulating the sequence of rig operationscomprises determining an amount of risk for constructing the portion ofthe wellbore by the first drilling.
 12. The method of claim 1, furthercomprising: comparing the clean time and cost generated using the one ormore regression analyses with an observed clean time and cost; andupdating the model based on the comparing.
 13. A system, comprising: acomputer processor; and a memory coupled to the computer processor andexecutable by the computer processor, the memory comprisingfunctionality for: obtaining geographic location data regarding of adesired geographic location for a first drilling rig; obtaining rigoperation data regarding a plurality of drilling rigs at differentgeographic locations; performing one or more regression analyses on therig operation data with a dependency on time, cost, or a combinationthereof so as to filter the rig operation data and generate an estimatedrange of clean time, non-productive time, and cost; generating, usingthe rig operation data, a model that identifies a level of riskassociated with a plurality of rig operations; simulating, by thecomputer processor and using the geographic location data and the model,a sequence of rig operations for constructing a portion of a wellboredrilled by the first drilling rig at the desired geographic locationbased at least in part on the model and the one or more regressionanalyses; determining a precision of the model based on a comparison ofthe estimated range of clean time and non-productive time with ameasured clean time and a measured non-productive time, respectively;adjusting the model, using an artificial intelligence algorithm, basedon a comparison of operation parameters of the model and the rigoperation data; and generating a control signal to operate an equipmentbased at least in part on the results of the simulating, whereinoperation of the equipment changes in response to receiving the controlsignal.
 14. The system of claim 13, wherein the sequence of rigoperations are simulated prior to construction of the wellbore at thedesired geographic location, and wherein the sequence of rig operationcomprises surface operations and downhole operations at the desiredgeographic location.
 15. The system of claim 13, wherein generating themodel comprises: obtaining the model; and updating the model in realtime based on the rig operation data, wherein the model is updatediteratively using a search method until the model converges to apredetermined criterion.
 16. The system of claim 13, wherein generatingthe model comprises using an artificial intelligence algorithm on therig operation data, and wherein the artificial intelligence algorithm isselected from a group consisting of a decision tree algorithm, a supportvector machine, an ensemble method, and a naive Bayes classifieralgorithm.
 17. The system of claim 13, wherein simulating the sequenceof rig operations comprises determining an amount of risk forconstructing the portion of the wellbore by the first drilling rig basedon a simulation of the sequence of rig operations.
 18. The system ofclaim 13, wherein the operations further comprise adjusting theoperation of the equipment responsive to the control signal.
 19. Anon-transitory computer readable medium storing instructions executableby a computer processor, the instructions comprising functionality for:obtaining geographic location data regarding of a desired geographiclocation for a first drilling rig; obtaining rig operation dataregarding a plurality of drilling rigs at different geographiclocations; performing one or more regression analyses on the rigoperation data with a dependency on time, cost, or a combination thereofso as to filter the rig operation data and generate an estimated rangeof clean time, non-productive time, and cost; generating, using the rigoperation data, a model that identifies a level of risk associated witha plurality of rig operations; determining a precision of the modelbased on a comparison of the estimated range of clean time andnon-productive time with a measured clean time and a measurednon-productive time, respectively; simulating, by the computer processorand using the geographic location data and the model, a sequence of rigoperations for constructing a portion of a wellbore drilled by the firstdrilling rig at the desired geographic location based at least in parton the model and the one or more regression analyses; adjusting themodel, using an artificial intelligence algorithm, based on a comparisonof operation parameters of the model and the rig operation data;generating a control signal to operate an equipment based at least inpart on the results of the simulating, wherein operation of theequipment changes in response to receiving the control signal;generating a risk proposal message that describes the probability that adrilling project will exceed a proposed bid based at least in part on aresult of the simulating; and displaying the risk proposal message to anoperator.
 20. The non-transitory computer readable medium of claim 19,wherein the sequence of rig operations are simulated prior toconstruction of the wellbore at the desired geographic location, andwherein the sequence of rig operation comprises surface operations anddownhole operations at the desired geographic location.
 21. Thenon-transitory computer readable medium of claim 19, wherein simulatingthe sequence of rig operations comprises determining an amount of riskfor constructing the portion of the wellbore by the first drilling rigbased on a simulation of the sequence of rig operations.